Abstract
Gait as biometrics has been widely used for human identification. However, direction changes cause difficulties for most of the gait-recognition systems, due to appearance changes. This study presents an efficient multi-view gait-recognition method that allows curved trajectories on completely unconstrained paths for indoor environments. Our method is based on volumetric reconstructions of humans, aligned along their way. A new gait descriptor, termed as gait entropy volume (GEnV), is also proposed. GEnV focuses on capturing 3D dynamical information of walking humans through the concept of entropy. Our approach does not require the sequence to be split into gait cycles. A GEnV-based signature is computed on the basis of the previous 3D gait volumes. Each signature is classified by a support vector machine, and a majority voting policy is used to smooth and reinforce the classifications results. The proposed approach is experimentally validated on the "AVA Multi-View Gait Dataset (AVAMVG)" and on the "Kyushu University 4D Gait Database (KY4D)". The results show that this new approach achieves promising results in the problem of gait recognition on unconstrained paths.
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